Overview

Dataset statistics

Number of variables27
Number of observations431101
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory108.2 MiB
Average record size in memory263.2 B

Variable types

Numeric13
Categorical14

Alerts

month has a high cardinality: 251 distinct valuesHigh cardinality
block has a high cardinality: 2472 distinct valuesHigh cardinality
street_name has a high cardinality: 553 distinct valuesHigh cardinality
resale_price is highly overall correlated with floor_area_sqmHigh correlation
distance_to_mrt_km is highly overall correlated with distance_to_mrt_binsHigh correlation
population_count is highly overall correlated with adult_count and 8 other fieldsHigh correlation
adult_count is highly overall correlated with population_count and 8 other fieldsHigh correlation
children_count is highly overall correlated with population_count and 8 other fieldsHigh correlation
senior_citizen_count is highly overall correlated with population_count and 8 other fieldsHigh correlation
teenager_count is highly overall correlated with population_count and 8 other fieldsHigh correlation
young_adult_count is highly overall correlated with population_count and 8 other fieldsHigh correlation
female_count is highly overall correlated with population_count and 8 other fieldsHigh correlation
male_count is highly overall correlated with population_count and 8 other fieldsHigh correlation
male_female_ratio is highly overall correlated with male_female_ratio_binsHigh correlation
floor_area_sqm is highly overall correlated with resale_price and 1 other fieldsHigh correlation
mrt_interchange_flag is highly overall correlated with mrt_interchange_count and 1 other fieldsHigh correlation
mrt_interchange_count is highly overall correlated with mrt_interchange_flag and 1 other fieldsHigh correlation
distance_to_mrt_bins is highly overall correlated with distance_to_mrt_km and 1 other fieldsHigh correlation
codes_name is highly overall correlated with distance_to_mrt_bins and 1 other fieldsHigh correlation
male_female_ratio_bins is highly overall correlated with male_female_ratioHigh correlation
population_bins is highly overall correlated with population_count and 8 other fieldsHigh correlation
town is highly overall correlated with population_count and 11 other fieldsHigh correlation
flat_type is highly overall correlated with floor_area_sqm and 1 other fieldsHigh correlation
flat_model is highly overall correlated with flat_typeHigh correlation
mrt_lrt_links is highly imbalanced (89.8%)Imbalance
mrt_interchange_flag is highly imbalanced (53.1%)Imbalance
mrt_interchange_count is highly imbalanced (53.1%)Imbalance
distance_to_mrt_bins is highly imbalanced (72.6%)Imbalance
male_female_ratio_bins is highly imbalanced (99.6%)Imbalance

Reproduction

Analysis started2023-03-19 01:00:26.765448
Analysis finished2023-03-19 01:01:16.662892
Duration49.9 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

resale_price
Real number (ℝ)

Distinct6404
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean301827.52
Minimum29700
Maximum1123200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2023-03-19T09:01:16.731816image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum29700
5-th percentile131400
Q1205200
median283950
Q3373500
95-th percentile544500
Maximum1123200
Range1093500
Interquartile range (IQR)168300

Descriptive statistics

Standard deviation129867.78
Coefficient of variation (CV)0.43027149
Kurtosis1.4130646
Mean301827.52
Median Absolute Deviation (MAD)83250
Skewness0.99048871
Sum1.3011814 × 1011
Variance1.6865639 × 1010
MonotonicityNot monotonic
2023-03-19T09:01:16.825463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
270000 3770
 
0.9%
315000 3710
 
0.9%
252000 3602
 
0.8%
288000 3601
 
0.8%
360000 3508
 
0.8%
342000 3472
 
0.8%
324000 3417
 
0.8%
297000 3369
 
0.8%
225000 3310
 
0.8%
243000 3159
 
0.7%
Other values (6394) 396183
91.9%
ValueCountFrequency (%)
29700 1
 
< 0.1%
31500 2
 
< 0.1%
32400 1
 
< 0.1%
33300 3
< 0.1%
34200 2
 
< 0.1%
36000 3
< 0.1%
36900 3
< 0.1%
37800 2
 
< 0.1%
38700 7
< 0.1%
39150 1
 
< 0.1%
ValueCountFrequency (%)
1123200 1
 
< 0.1%
1108800 1
 
< 0.1%
1087200 2
< 0.1%
1084500 1
 
< 0.1%
1080000 3
< 0.1%
1068199.2 1
 
< 0.1%
1062000 1
 
< 0.1%
1053000 2
< 0.1%
1051200 1
 
< 0.1%
1044799.2 1
 
< 0.1%

distance_to_mrt_km
Real number (ℝ)

Distinct9138
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65255648
Minimum0.022112447
Maximum3.5157764
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2023-03-19T09:01:17.102373image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.022112447
5-th percentile0.16641647
Q10.36759679
median0.58764487
Q30.8601159
95-th percentile1.3984273
Maximum3.5157764
Range3.4936639
Interquartile range (IQR)0.49251911

Descriptive statistics

Standard deviation0.38335854
Coefficient of variation (CV)0.58747182
Kurtosis1.6966049
Mean0.65255648
Median Absolute Deviation (MAD)0.24035296
Skewness1.0914727
Sum281317.75
Variance0.14696377
MonotonicityNot monotonic
2023-03-19T09:01:17.188723image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8806170356 336
 
0.1%
0.8804880701 329
 
0.1%
0.9620735662 312
 
0.1%
1.009626078 312
 
0.1%
0.8084054236 310
 
0.1%
0.9636654817 288
 
0.1%
1.213218379 281
 
0.1%
1.170511977 268
 
0.1%
0.6498798611 240
 
0.1%
0.7835843273 239
 
0.1%
Other values (9128) 428186
99.3%
ValueCountFrequency (%)
0.02211244716 64
< 0.1%
0.02648832499 33
< 0.1%
0.03602304855 33
< 0.1%
0.03905292277 3
 
< 0.1%
0.04148001859 6
 
< 0.1%
0.0434819053 28
 
< 0.1%
0.04348866364 33
< 0.1%
0.04372690318 76
< 0.1%
0.04389555737 59
< 0.1%
0.04432644791 74
< 0.1%
ValueCountFrequency (%)
3.515776371 20
 
< 0.1%
3.491570856 61
< 0.1%
3.454113187 24
 
< 0.1%
2.150380755 95
< 0.1%
2.123572473 56
< 0.1%
2.101792667 77
< 0.1%
2.095430751 41
< 0.1%
2.089538192 41
< 0.1%
2.080300714 56
< 0.1%
2.080200347 54
< 0.1%

mrt_lrt_links
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
0
425350 
1
 
5751

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters431101
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 425350
98.7%
1 5751
 
1.3%

Length

2023-03-19T09:01:17.266981image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-19T09:01:17.345564image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 425350
98.7%
1 5751
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 425350
98.7%
1 5751
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 431101
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 425350
98.7%
1 5751
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
Common 431101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 425350
98.7%
1 5751
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 431101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 425350
98.7%
1 5751
 
1.3%

mrt_interchange_flag
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
0
388051 
1
43050 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters431101
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 388051
90.0%
1 43050
 
10.0%

Length

2023-03-19T09:01:17.401018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-19T09:01:17.469416image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 388051
90.0%
1 43050
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 388051
90.0%
1 43050
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 431101
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 388051
90.0%
1 43050
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common 431101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 388051
90.0%
1 43050
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 431101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 388051
90.0%
1 43050
 
10.0%

mrt_interchange_count
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
0
388051 
1
43050 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters431101
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 388051
90.0%
1 43050
 
10.0%

Length

2023-03-19T09:01:17.526404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-19T09:01:17.591482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 388051
90.0%
1 43050
 
10.0%

Most occurring characters

ValueCountFrequency (%)
0 388051
90.0%
1 43050
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 431101
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 388051
90.0%
1 43050
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Common 431101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 388051
90.0%
1 43050
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 431101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 388051
90.0%
1 43050
 
10.0%

distance_to_mrt_bins
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
0
392974 
1
 
38022
2
 
105

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters431101
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 392974
91.2%
1 38022
 
8.8%
2 105
 
< 0.1%

Length

2023-03-19T09:01:17.646015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-19T09:01:17.713280image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 392974
91.2%
1 38022
 
8.8%
2 105
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 392974
91.2%
1 38022
 
8.8%
2 105
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 431101
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 392974
91.2%
1 38022
 
8.8%
2 105
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 431101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 392974
91.2%
1 38022
 
8.8%
2 105
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 431101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 392974
91.2%
1 38022
 
8.8%
2 105
 
< 0.1%

codes_name
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
NS
134598 
EW
126423 
DT
44968 
NE
38869 
BP
25244 
Other values (9)
60999 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters862202
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEW
2nd rowEW
3rd rowEW
4th rowEW
5th rowEW

Common Values

ValueCountFrequency (%)
NS 134598
31.2%
EW 126423
29.3%
DT 44968
 
10.4%
NE 38869
 
9.0%
BP 25244
 
5.9%
CC 24924
 
5.8%
SE 10093
 
2.3%
SW 9570
 
2.2%
PE 8245
 
1.9%
TE 6205
 
1.4%
Other values (4) 1962
 
0.5%

Length

2023-03-19T09:01:17.776996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ns 134598
31.2%
ew 126423
29.3%
dt 44968
 
10.4%
ne 38869
 
9.0%
bp 25244
 
5.9%
cc 24924
 
5.8%
se 10093
 
2.3%
sw 9570
 
2.2%
pe 8245
 
1.9%
te 6205
 
1.4%
Other values (4) 1962
 
0.5%

Most occurring characters

ValueCountFrequency (%)
E 189835
22.0%
N 173467
20.1%
S 155111
18.0%
W 136873
15.9%
T 52150
 
6.0%
C 49953
 
5.8%
D 44968
 
5.2%
P 34496
 
4.0%
B 25244
 
2.9%
G 105
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 862202
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 189835
22.0%
N 173467
20.1%
S 155111
18.0%
W 136873
15.9%
T 52150
 
6.0%
C 49953
 
5.8%
D 44968
 
5.2%
P 34496
 
4.0%
B 25244
 
2.9%
G 105
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 862202
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 189835
22.0%
N 173467
20.1%
S 155111
18.0%
W 136873
15.9%
T 52150
 
6.0%
C 49953
 
5.8%
D 44968
 
5.2%
P 34496
 
4.0%
B 25244
 
2.9%
G 105
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 862202
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 189835
22.0%
N 173467
20.1%
S 155111
18.0%
W 136873
15.9%
T 52150
 
6.0%
C 49953
 
5.8%
D 44968
 
5.2%
P 34496
 
4.0%
B 25244
 
2.9%
G 105
 
< 0.1%

population_count
Real number (ℝ)

Distinct153
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42449.893
Minimum0
Maximum138490
Zeros154
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2023-03-19T09:01:17.845925image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10880
Q123940
median33410
Q354880
95-th percentile95530
Maximum138490
Range138490
Interquartile range (IQR)30940

Descriptive statistics

Standard deviation30254.682
Coefficient of variation (CV)0.71271515
Kurtosis2.3857976
Mean42449.893
Median Absolute Deviation (MAD)14910
Skewness1.5828158
Sum1.8300191 × 1010
Variance9.1534579 × 108
MonotonicityNot monotonic
2023-03-19T09:01:17.925521image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
138490 19540
 
4.5%
95530 16380
 
3.8%
85930 12244
 
2.8%
78150 10620
 
2.5%
57890 9774
 
2.3%
68240 8451
 
2.0%
70930 8052
 
1.9%
56880 7821
 
1.8%
59510 7447
 
1.7%
50130 7170
 
1.7%
Other values (143) 323602
75.1%
ValueCountFrequency (%)
0 154
< 0.1%
130 38
 
< 0.1%
810 105
< 0.1%
910 72
 
< 0.1%
1210 142
< 0.1%
1490 254
0.1%
1520 143
< 0.1%
1580 186
< 0.1%
2080 259
0.1%
2330 80
 
< 0.1%
ValueCountFrequency (%)
138490 19540
4.5%
95530 16380
3.8%
85930 12244
2.8%
78150 10620
2.5%
70930 8052
1.9%
68240 8451
2.0%
59830 5526
 
1.3%
59510 7447
 
1.7%
57890 9774
2.3%
56880 7821
1.8%

adult_count
Real number (ℝ)

Distinct152
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22465.604
Minimum0
Maximum72740
Zeros154
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2023-03-19T09:01:18.015231image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5920
Q112660
median17440
Q329740
95-th percentile50520
Maximum72740
Range72740
Interquartile range (IQR)17080

Descriptive statistics

Standard deviation15955.375
Coefficient of variation (CV)0.71021348
Kurtosis2.2610167
Mean22465.604
Median Absolute Deviation (MAD)7720
Skewness1.5526044
Sum9.6849444 × 109
Variance2.5457399 × 108
MonotonicityNot monotonic
2023-03-19T09:01:18.099645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72740 19540
 
4.5%
50520 16380
 
3.8%
45400 12244
 
2.8%
40960 10620
 
2.5%
31660 9774
 
2.3%
37740 8451
 
2.0%
16570 8272
 
1.9%
37240 8052
 
1.9%
30600 7821
 
1.8%
30090 7447
 
1.7%
Other values (142) 322500
74.8%
ValueCountFrequency (%)
0 154
< 0.1%
20 38
 
< 0.1%
440 105
< 0.1%
490 72
 
< 0.1%
590 142
< 0.1%
800 143
< 0.1%
830 254
0.1%
890 186
< 0.1%
1140 259
0.1%
1180 179
< 0.1%
ValueCountFrequency (%)
72740 19540
4.5%
50520 16380
3.8%
45400 12244
2.8%
40960 10620
2.5%
37740 8451
2.0%
37240 8052
1.9%
31990 5526
 
1.3%
31660 9774
2.3%
30600 7821
1.8%
30090 7447
 
1.7%

children_count
Real number (ℝ)

Distinct135
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4146.2326
Minimum0
Maximum12040
Zeros192
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2023-03-19T09:01:18.187824image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile930
Q12020
median3150
Q36140
95-th percentile11140
Maximum12040
Range12040
Interquartile range (IQR)4120

Descriptive statistics

Standard deviation3025.5707
Coefficient of variation (CV)0.72971562
Kurtosis0.69037562
Mean4146.2326
Median Absolute Deviation (MAD)1630
Skewness1.2130176
Sum1.787445 × 109
Variance9154077.8
MonotonicityNot monotonic
2023-03-19T09:01:18.277435image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12040 19540
 
4.5%
11140 16380
 
3.8%
4780 12677
 
2.9%
6960 12244
 
2.8%
2820 11408
 
2.6%
7240 10620
 
2.5%
4830 9774
 
2.3%
9060 8451
 
2.0%
6470 8052
 
1.9%
2330 7901
 
1.8%
Other values (125) 314054
72.8%
ValueCountFrequency (%)
0 192
< 0.1%
50 105
 
< 0.1%
60 72
 
< 0.1%
70 186
 
< 0.1%
100 259
0.1%
110 142
 
< 0.1%
120 477
0.1%
200 179
 
< 0.1%
240 397
0.1%
290 333
0.1%
ValueCountFrequency (%)
12040 19540
4.5%
11140 16380
3.8%
9060 8451
2.0%
7800 6045
 
1.4%
7240 10620
2.5%
7060 2859
 
0.7%
6960 12244
2.8%
6890 5526
 
1.3%
6750 5799
 
1.3%
6740 4913
 
1.1%

senior_citizen_count
Real number (ℝ)

Distinct128
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2594.3564
Minimum0
Maximum8130
Zeros154
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2023-03-19T09:01:18.370216image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile770
Q11410
median2140
Q33130
95-th percentile7830
Maximum8130
Range8130
Interquartile range (IQR)1720

Descriptive statistics

Standard deviation1817.3955
Coefficient of variation (CV)0.70051885
Kurtosis3.1294519
Mean2594.3564
Median Absolute Deviation (MAD)820
Skewness1.8278975
Sum1.1184296 × 109
Variance3302926.6
MonotonicityNot monotonic
2023-03-19T09:01:18.458213image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8130 19540
 
4.5%
2980 16380
 
3.8%
7830 12244
 
2.8%
880 11510
 
2.7%
4260 10620
 
2.5%
2160 10151
 
2.4%
3850 9774
 
2.3%
1410 8238
 
1.9%
2770 8052
 
1.9%
3550 7821
 
1.8%
Other values (118) 316771
73.5%
ValueCountFrequency (%)
0 154
 
< 0.1%
50 92
 
< 0.1%
70 72
 
< 0.1%
90 38
 
< 0.1%
100 248
 
0.1%
140 142
 
< 0.1%
190 858
0.2%
200 72
 
< 0.1%
240 186
 
< 0.1%
260 1113
0.3%
ValueCountFrequency (%)
8130 19540
4.5%
7830 12244
2.8%
5320 4044
 
0.9%
4260 10620
2.5%
4130 3458
 
0.8%
3980 3216
 
0.7%
3850 9774
2.3%
3770 3000
 
0.7%
3700 3239
 
0.8%
3580 3133
 
0.7%

teenager_count
Real number (ℝ)

Distinct138
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5232.3996
Minimum0
Maximum16780
Zeros192
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2023-03-19T09:01:18.554882image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1020
Q12470
median3950
Q36480
95-th percentile14940
Maximum16780
Range16780
Interquartile range (IQR)4010

Descriptive statistics

Standard deviation4062.0893
Coefficient of variation (CV)0.77633392
Kurtosis1.5473396
Mean5232.3996
Median Absolute Deviation (MAD)2030
Skewness1.4376296
Sum2.2556927 × 109
Variance16500570
MonotonicityNot monotonic
2023-03-19T09:01:18.641568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16780 19540
 
4.5%
14940 16380
 
3.8%
9100 12244
 
2.8%
9730 10620
 
2.5%
6480 9774
 
2.3%
8320 8451
 
2.0%
10130 8052
 
1.9%
6730 7821
 
1.8%
8970 7447
 
1.7%
5360 7170
 
1.7%
Other values (128) 323602
75.1%
ValueCountFrequency (%)
0 192
< 0.1%
80 258
0.1%
90 105
 
< 0.1%
110 254
0.1%
120 401
0.1%
140 80
 
< 0.1%
160 143
 
< 0.1%
240 179
< 0.1%
270 333
0.1%
340 397
0.1%
ValueCountFrequency (%)
16780 19540
4.5%
14940 16380
3.8%
10130 8052
1.9%
9730 10620
2.5%
9100 12244
2.8%
8970 7447
 
1.7%
8320 8451
2.0%
8210 5526
 
1.3%
7420 5799
 
1.3%
6730 7821
1.8%

young_adult_count
Real number (ℝ)

Distinct135
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6162.8348
Minimum0
Maximum22580
Zeros192
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2023-03-19T09:01:18.735601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1240
Q13170
median4610
Q38140
95-th percentile13590
Maximum22580
Range22580
Interquartile range (IQR)4970

Descriptive statistics

Standard deviation4875.1849
Coefficient of variation (CV)0.79106207
Kurtosis3.3826248
Mean6162.8348
Median Absolute Deviation (MAD)2110
Skewness1.7901974
Sum2.6568042 × 109
Variance23767427
MonotonicityNot monotonic
2023-03-19T09:01:18.945927image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22580 19540
 
4.5%
13590 16380
 
3.8%
11290 12244
 
2.8%
12630 10620
 
2.5%
8480 9774
 
2.3%
5320 9042
 
2.1%
8810 8451
 
2.0%
2290 8354
 
1.9%
11690 8052
 
1.9%
5220 7849
 
1.8%
Other values (125) 320795
74.4%
ValueCountFrequency (%)
0 192
 
< 0.1%
80 105
 
< 0.1%
150 654
0.2%
220 259
 
0.1%
240 80
 
< 0.1%
270 143
 
< 0.1%
290 179
 
< 0.1%
320 333
0.1%
390 79
 
< 0.1%
440 489
0.1%
ValueCountFrequency (%)
22580 19540
4.5%
13590 16380
3.8%
12630 10620
2.5%
11690 8052
1.9%
11290 12244
2.8%
10450 7447
 
1.7%
8810 8451
2.0%
8620 6045
 
1.4%
8480 9774
2.3%
8440 7170
 
1.7%

female_count
Real number (ℝ)

Distinct147
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21483.474
Minimum0
Maximum70600
Zeros154
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2023-03-19T09:01:19.033075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5600
Q112470
median16840
Q327770
95-th percentile47670
Maximum70600
Range70600
Interquartile range (IQR)15300

Descriptive statistics

Standard deviation15300.622
Coefficient of variation (CV)0.71220428
Kurtosis2.5636748
Mean21483.474
Median Absolute Deviation (MAD)7420
Skewness1.621204
Sum9.2615472 × 109
Variance2.3410904 × 108
MonotonicityNot monotonic
2023-03-19T09:01:19.115966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70600 19540
 
4.5%
47670 16380
 
3.8%
43950 12244
 
2.8%
39520 10620
 
2.5%
29080 9774
 
2.3%
17890 9156
 
2.1%
33940 8451
 
2.0%
34890 8052
 
1.9%
28490 7821
 
1.8%
29980 7447
 
1.7%
Other values (137) 321616
74.6%
ValueCountFrequency (%)
0 154
< 0.1%
80 38
 
< 0.1%
390 105
< 0.1%
450 72
 
< 0.1%
640 142
< 0.1%
760 143
< 0.1%
780 254
0.1%
820 186
< 0.1%
1090 259
0.1%
1200 179
< 0.1%
ValueCountFrequency (%)
70600 19540
4.5%
47670 16380
3.8%
43950 12244
2.8%
39520 10620
2.5%
34890 8052
1.9%
33940 8451
2.0%
30240 5526
 
1.3%
29980 7447
 
1.7%
29080 9774
2.3%
28490 7821
1.8%

male_count
Real number (ℝ)

Distinct146
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20966.419
Minimum0
Maximum67890
Zeros154
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2023-03-19T09:01:19.210895image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5380
Q111920
median16320
Q327200
95-th percentile47860
Maximum67890
Range67890
Interquartile range (IQR)15280

Descriptive statistics

Standard deviation14962.227
Coefficient of variation (CV)0.71362817
Kurtosis2.2049466
Mean20966.419
Median Absolute Deviation (MAD)7240
Skewness1.5432075
Sum9.0386443 × 109
Variance2.2386825 × 108
MonotonicityNot monotonic
2023-03-19T09:01:19.293082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67890 19540
 
4.5%
47860 16380
 
3.8%
41980 12244
 
2.8%
38630 10620
 
2.5%
17960 10002
 
2.3%
28810 9774
 
2.3%
34300 8451
 
2.0%
36040 8052
 
1.9%
28390 7821
 
1.8%
29530 7447
 
1.7%
Other values (136) 320770
74.4%
ValueCountFrequency (%)
0 154
< 0.1%
50 38
 
< 0.1%
420 105
 
< 0.1%
460 72
 
< 0.1%
570 142
< 0.1%
710 254
0.1%
760 329
0.1%
990 259
0.1%
1110 80
 
< 0.1%
1240 179
< 0.1%
ValueCountFrequency (%)
67890 19540
4.5%
47860 16380
3.8%
41980 12244
2.8%
38630 10620
2.5%
36040 8052
1.9%
34300 8451
2.0%
29590 5526
 
1.3%
29530 7447
 
1.7%
28810 9774
2.3%
28390 7821
1.8%

male_female_ratio
Real number (ℝ)

Distinct155
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97108889
Minimum0
Maximum1.2362385
Zeros154
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2023-03-19T09:01:19.391297image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.89095128
Q10.95517634
median0.97747976
Q30.99649
95-th percentile1.0329607
Maximum1.2362385
Range1.2362385
Interquartile range (IQR)0.04131366

Descriptive statistics

Standard deviation0.044491102
Coefficient of variation (CV)0.045815684
Kurtosis82.297657
Mean0.97108889
Median Absolute Deviation (MAD)0.022153297
Skewness-3.835844
Sum418637.39
Variance0.0019794581
MonotonicityNot monotonic
2023-03-19T09:01:19.483577image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9616147309 19540
 
4.5%
1.003985735 16380
 
3.8%
0.9551763367 12244
 
2.8%
0.9774797571 10620
 
2.5%
0.9907152682 9774
 
2.3%
1.010606953 8451
 
2.0%
1.032960734 8052
 
1.9%
0.9964899965 7821
 
1.8%
0.9849899933 7447
 
1.7%
0.9775147929 7170
 
1.7%
Other values (145) 323602
75.1%
ValueCountFrequency (%)
0 154
 
< 0.1%
0.625 38
 
< 0.1%
0.8447606727 2037
0.5%
0.854368932 1766
0.4%
0.8550295858 170
 
< 0.1%
0.8668639053 516
 
0.1%
0.8670212766 79
 
< 0.1%
0.8756218905 672
 
0.2%
0.8771121352 1027
 
0.2%
0.877842755 3239
0.8%
ValueCountFrequency (%)
1.236238532 485
 
0.1%
1.174603175 254
 
0.1%
1.115384615 397
 
0.1%
1.103869654 497
 
0.1%
1.086092715 333
 
0.1%
1.076923077 105
 
< 0.1%
1.051417271 3654
0.8%
1.05046729 4481
1.0%
1.04 1113
 
0.3%
1.038997214 2431
0.6%

male_female_ratio_bins
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
2
430909 
0
 
154
1
 
38

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters431101
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 430909
> 99.9%
0 154
 
< 0.1%
1 38
 
< 0.1%

Length

2023-03-19T09:01:19.558157image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-19T09:01:19.631635image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
2 430909
> 99.9%
0 154
 
< 0.1%
1 38
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 430909
> 99.9%
0 154
 
< 0.1%
1 38
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 431101
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 430909
> 99.9%
0 154
 
< 0.1%
1 38
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 431101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 430909
> 99.9%
0 154
 
< 0.1%
1 38
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 431101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 430909
> 99.9%
0 154
 
< 0.1%
1 38
 
< 0.1%

population_bins
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
0
302188 
1
92993 
2
35920 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters431101
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 302188
70.1%
1 92993
 
21.6%
2 35920
 
8.3%

Length

2023-03-19T09:01:19.689913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-19T09:01:19.760806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0 302188
70.1%
1 92993
 
21.6%
2 35920
 
8.3%

Most occurring characters

ValueCountFrequency (%)
0 302188
70.1%
1 92993
 
21.6%
2 35920
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 431101
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 302188
70.1%
1 92993
 
21.6%
2 35920
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Common 431101
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 302188
70.1%
1 92993
 
21.6%
2 35920
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 431101
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 302188
70.1%
1 92993
 
21.6%
2 35920
 
8.3%

month
Categorical

Distinct251
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
2010-07
 
2924
2010-06
 
2782
2009-10
 
2763
2001-10
 
2762
2002-01
 
2672
Other values (246)
417198 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters3017707
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2001-08
2nd row2002-09
3rd row2020-10
4th row2010-10
5th row2002-08

Common Values

ValueCountFrequency (%)
2010-07 2924
 
0.7%
2010-06 2782
 
0.6%
2009-10 2763
 
0.6%
2001-10 2762
 
0.6%
2002-01 2672
 
0.6%
2001-07 2661
 
0.6%
2001-11 2653
 
0.6%
2000-11 2631
 
0.6%
2001-06 2626
 
0.6%
2002-05 2605
 
0.6%
Other values (241) 404022
93.7%

Length

2023-03-19T09:01:19.827247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2010-07 2924
 
0.7%
2010-06 2782
 
0.6%
2009-10 2763
 
0.6%
2001-10 2762
 
0.6%
2002-01 2672
 
0.6%
2001-07 2661
 
0.6%
2001-11 2653
 
0.6%
2000-11 2631
 
0.6%
2001-06 2626
 
0.6%
2002-05 2605
 
0.6%
Other values (241) 404022
93.7%

Most occurring characters

ValueCountFrequency (%)
0 1109474
36.8%
2 556694
18.4%
- 431101
 
14.3%
1 396301
 
13.1%
9 79136
 
2.6%
7 76689
 
2.5%
8 76633
 
2.5%
6 74718
 
2.5%
5 73327
 
2.4%
3 72821
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2586606
85.7%
Dash Punctuation 431101
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1109474
42.9%
2 556694
21.5%
1 396301
 
15.3%
9 79136
 
3.1%
7 76689
 
3.0%
8 76633
 
3.0%
6 74718
 
2.9%
5 73327
 
2.8%
3 72821
 
2.8%
4 70813
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 431101
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3017707
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1109474
36.8%
2 556694
18.4%
- 431101
 
14.3%
1 396301
 
13.1%
9 79136
 
2.6%
7 76689
 
2.5%
8 76633
 
2.5%
6 74718
 
2.5%
5 73327
 
2.4%
3 72821
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3017707
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1109474
36.8%
2 556694
18.4%
- 431101
 
14.3%
1 396301
 
13.1%
9 79136
 
2.6%
7 76689
 
2.5%
8 76633
 
2.5%
6 74718
 
2.5%
5 73327
 
2.4%
3 72821
 
2.4%

town
Categorical

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
woodlands
39195 
tampines
34059 
jurong west
32832 
yishun
28652 
bedok
27558 
Other values (21)
268805 

Length

Max length15
Median length12
Mean length9.1001436
Min length5

Characters and Unicode

Total characters3923081
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowpasir ris
2nd rowpasir ris
3rd rowpasir ris
4th rowpasir ris
5th rowpasir ris

Common Values

ValueCountFrequency (%)
woodlands 39195
 
9.1%
tampines 34059
 
7.9%
jurong west 32832
 
7.6%
yishun 28652
 
6.6%
bedok 27558
 
6.4%
hougang 23810
 
5.5%
ang mo kio 22230
 
5.2%
choa chu kang 19970
 
4.6%
sengkang 19363
 
4.5%
bukit batok 19269
 
4.5%
Other values (16) 164163
38.1%

Length

2023-03-19T09:01:19.889853image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
bukit 51173
 
7.9%
jurong 43794
 
6.8%
woodlands 39195
 
6.1%
tampines 34059
 
5.3%
west 32832
 
5.1%
yishun 28652
 
4.4%
bedok 27558
 
4.3%
hougang 23810
 
3.7%
kio 22230
 
3.4%
mo 22230
 
3.4%
Other values (26) 320973
49.6%

Most occurring characters

ValueCountFrequency (%)
a 411997
 
10.5%
n 394668
 
10.1%
o 339649
 
8.7%
g 261854
 
6.7%
s 236963
 
6.0%
e 231906
 
5.9%
215405
 
5.5%
i 192002
 
4.9%
t 190399
 
4.9%
u 190111
 
4.8%
Other values (14) 1258127
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3696560
94.2%
Space Separator 215405
 
5.5%
Other Punctuation 11116
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 411997
 
11.1%
n 394668
 
10.7%
o 339649
 
9.2%
g 261854
 
7.1%
s 236963
 
6.4%
e 231906
 
6.3%
i 192002
 
5.2%
t 190399
 
5.2%
u 190111
 
5.1%
k 170679
 
4.6%
Other values (12) 1076332
29.1%
Space Separator
ValueCountFrequency (%)
215405
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 11116
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3696560
94.2%
Common 226521
 
5.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 411997
 
11.1%
n 394668
 
10.7%
o 339649
 
9.2%
g 261854
 
7.1%
s 236963
 
6.4%
e 231906
 
6.3%
i 192002
 
5.2%
t 190399
 
5.2%
u 190111
 
5.1%
k 170679
 
4.6%
Other values (12) 1076332
29.1%
Common
ValueCountFrequency (%)
215405
95.1%
/ 11116
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3923081
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 411997
 
10.5%
n 394668
 
10.1%
o 339649
 
8.7%
g 261854
 
6.7%
s 236963
 
6.0%
e 231906
 
5.9%
215405
 
5.5%
i 192002
 
4.9%
t 190399
 
4.9%
u 190111
 
4.8%
Other values (14) 1258127
32.1%

flat_type
Categorical

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
4-room
169344 
3-room
125485 
5-room
98589 
executive
32710 
2-room
 
4507
Other values (2)
 
466

Length

Max length16
Median length6
Mean length6.2319178
Min length6

Characters and Unicode

Total characters2686586
Distinct characters20
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4-room
2nd row4-room
3rd rowexecutive
4th row4-room
5th row4-room

Common Values

ValueCountFrequency (%)
4-room 169344
39.3%
3-room 125485
29.1%
5-room 98589
22.9%
executive 32710
 
7.6%
2-room 4507
 
1.0%
1-room 281
 
0.1%
multi-generation 185
 
< 0.1%

Length

2023-03-19T09:01:19.953094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-19T09:01:20.031366image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
4-room 169344
39.3%
3-room 125485
29.1%
5-room 98589
22.9%
executive 32710
 
7.6%
2-room 4507
 
1.0%
1-room 281
 
0.1%
multi-generation 185
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 796597
29.7%
- 398391
14.8%
r 398391
14.8%
m 398391
14.8%
4 169344
 
6.3%
3 125485
 
4.7%
5 98589
 
3.7%
e 98500
 
3.7%
i 33080
 
1.2%
t 33080
 
1.2%
Other values (10) 136738
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1889989
70.3%
Dash Punctuation 398391
 
14.8%
Decimal Number 398206
 
14.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 796597
42.1%
r 398391
21.1%
m 398391
21.1%
e 98500
 
5.2%
i 33080
 
1.8%
t 33080
 
1.8%
u 32895
 
1.7%
c 32710
 
1.7%
x 32710
 
1.7%
v 32710
 
1.7%
Other values (4) 925
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
4 169344
42.5%
3 125485
31.5%
5 98589
24.8%
2 4507
 
1.1%
1 281
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 398391
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1889989
70.3%
Common 796597
29.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 796597
42.1%
r 398391
21.1%
m 398391
21.1%
e 98500
 
5.2%
i 33080
 
1.8%
t 33080
 
1.8%
u 32895
 
1.7%
c 32710
 
1.7%
x 32710
 
1.7%
v 32710
 
1.7%
Other values (4) 925
 
< 0.1%
Common
ValueCountFrequency (%)
- 398391
50.0%
4 169344
21.3%
3 125485
 
15.8%
5 98589
 
12.4%
2 4507
 
0.6%
1 281
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2686586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 796597
29.7%
- 398391
14.8%
r 398391
14.8%
m 398391
14.8%
4 169344
 
6.3%
3 125485
 
4.7%
5 98589
 
3.7%
e 98500
 
3.7%
i 33080
 
1.2%
t 33080
 
1.2%
Other values (10) 136738
 
5.1%

block
Categorical

Distinct2472
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
1
 
1668
2
 
1641
101
 
1424
110
 
1421
114
 
1405
Other values (2467)
423542 

Length

Max length4
Median length3
Mean length2.9715867
Min length1

Characters and Unicode

Total characters1281054
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st row440
2nd row473
3rd row220
4th row474
5th row751

Common Values

ValueCountFrequency (%)
1 1668
 
0.4%
2 1641
 
0.4%
101 1424
 
0.3%
110 1421
 
0.3%
114 1405
 
0.3%
4 1399
 
0.3%
108 1381
 
0.3%
113 1378
 
0.3%
107 1352
 
0.3%
8 1325
 
0.3%
Other values (2462) 416707
96.7%

Length

2023-03-19T09:01:20.112483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 1668
 
0.4%
2 1641
 
0.4%
101 1424
 
0.3%
110 1421
 
0.3%
114 1405
 
0.3%
4 1399
 
0.3%
108 1381
 
0.3%
113 1378
 
0.3%
107 1352
 
0.3%
8 1325
 
0.3%
Other values (2462) 416707
96.7%

Most occurring characters

ValueCountFrequency (%)
1 187614
14.6%
2 160804
12.6%
3 135639
10.6%
4 131187
10.2%
6 122898
9.6%
5 120296
9.4%
7 102452
8.0%
0 96358
7.5%
8 89308
7.0%
9 69294
 
5.4%
Other values (11) 65204
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1215850
94.9%
Uppercase Letter 65204
 
5.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 21295
32.7%
B 18354
28.1%
C 13865
21.3%
D 8170
 
12.5%
E 1610
 
2.5%
F 846
 
1.3%
G 584
 
0.9%
H 257
 
0.4%
J 120
 
0.2%
M 54
 
0.1%
Decimal Number
ValueCountFrequency (%)
1 187614
15.4%
2 160804
13.2%
3 135639
11.2%
4 131187
10.8%
6 122898
10.1%
5 120296
9.9%
7 102452
8.4%
0 96358
7.9%
8 89308
7.3%
9 69294
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
Common 1215850
94.9%
Latin 65204
 
5.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 21295
32.7%
B 18354
28.1%
C 13865
21.3%
D 8170
 
12.5%
E 1610
 
2.5%
F 846
 
1.3%
G 584
 
0.9%
H 257
 
0.4%
J 120
 
0.2%
M 54
 
0.1%
Common
ValueCountFrequency (%)
1 187614
15.4%
2 160804
13.2%
3 135639
11.2%
4 131187
10.8%
6 122898
10.1%
5 120296
9.9%
7 102452
8.4%
0 96358
7.9%
8 89308
7.3%
9 69294
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1281054
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 187614
14.6%
2 160804
12.6%
3 135639
10.6%
4 131187
10.2%
6 122898
9.6%
5 120296
9.4%
7 102452
8.0%
0 96358
7.5%
8 89308
7.0%
9 69294
 
5.4%
Other values (11) 65204
 
5.1%

street_name
Categorical

Distinct553
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
yishun ring road
 
7302
ang mo kio avenue 10
 
5868
bedok reservoir road
 
5651
ang mo kio avenue 3
 
5137
hougang avenue 8
 
4093
Other values (548)
403050 

Length

Max length25
Median length22
Mean length17.301932
Min length9

Characters and Unicode

Total characters7458880
Distinct characters37
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowpasir ris drive 4
2nd rowpasir ris drive 6
3rd rowpasir ris street 21
4th rowpasir ris drive 6
5th rowpasir ris street 71

Common Values

ValueCountFrequency (%)
yishun ring road 7302
 
1.7%
ang mo kio avenue 10 5868
 
1.4%
bedok reservoir road 5651
 
1.3%
ang mo kio avenue 3 5137
 
1.2%
hougang avenue 8 4093
 
0.9%
bedok north street 3 3237
 
0.8%
ang mo kio avenue 4 3180
 
0.7%
tampines street 21 3088
 
0.7%
woodlands ring road 2993
 
0.7%
bedok north road 2977
 
0.7%
Other values (543) 387575
89.9%

Length

2023-03-19T09:01:20.194594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
street 130884
 
9.5%
avenue 101100
 
7.4%
road 72655
 
5.3%
drive 38550
 
2.8%
west 36011
 
2.6%
woodlands 34460
 
2.5%
jurong 32498
 
2.4%
tampines 30160
 
2.2%
yishun 28652
 
2.1%
bukit 24793
 
1.8%
Other values (307) 841275
61.4%

Most occurring characters

ValueCountFrequency (%)
939937
12.6%
e 829922
 
11.1%
a 595967
 
8.0%
n 520936
 
7.0%
t 500404
 
6.7%
r 473790
 
6.4%
o 464176
 
6.2%
s 406563
 
5.5%
i 302188
 
4.1%
u 254979
 
3.4%
Other values (27) 2170018
29.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6128029
82.2%
Space Separator 939937
 
12.6%
Decimal Number 390133
 
5.2%
Other Punctuation 781
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 829922
13.5%
a 595967
 
9.7%
n 520936
 
8.5%
t 500404
 
8.2%
r 473790
 
7.7%
o 464176
 
7.6%
s 406563
 
6.6%
i 302188
 
4.9%
u 254979
 
4.2%
d 227198
 
3.7%
Other values (15) 1551906
25.3%
Decimal Number
ValueCountFrequency (%)
1 107457
27.5%
2 67734
17.4%
4 46972
12.0%
3 46605
11.9%
5 36135
 
9.3%
6 25531
 
6.5%
8 20294
 
5.2%
7 17602
 
4.5%
0 13228
 
3.4%
9 8575
 
2.2%
Space Separator
ValueCountFrequency (%)
939937
100.0%
Other Punctuation
ValueCountFrequency (%)
' 781
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6128029
82.2%
Common 1330851
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 829922
13.5%
a 595967
 
9.7%
n 520936
 
8.5%
t 500404
 
8.2%
r 473790
 
7.7%
o 464176
 
7.6%
s 406563
 
6.6%
i 302188
 
4.9%
u 254979
 
4.2%
d 227198
 
3.7%
Other values (15) 1551906
25.3%
Common
ValueCountFrequency (%)
939937
70.6%
1 107457
 
8.1%
2 67734
 
5.1%
4 46972
 
3.5%
3 46605
 
3.5%
5 36135
 
2.7%
6 25531
 
1.9%
8 20294
 
1.5%
7 17602
 
1.3%
0 13228
 
1.0%
Other values (2) 9356
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7458880
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
939937
12.6%
e 829922
 
11.1%
a 595967
 
8.0%
n 520936
 
7.0%
t 500404
 
6.7%
r 473790
 
6.4%
o 464176
 
6.2%
s 406563
 
5.5%
i 302188
 
4.1%
u 254979
 
3.4%
Other values (27) 2170018
29.1%

storey_range
Categorical

Distinct25
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
04 to 06
107075 
07 to 09
95601 
01 to 03
86135 
10 to 12
81498 
13 to 15
30810 
Other values (20)
29982 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters3448808
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01 to 03
2nd row04 to 06
3rd row10 to 12
4th row04 to 06
5th row04 to 06

Common Values

ValueCountFrequency (%)
04 to 06 107075
24.8%
07 to 09 95601
22.2%
01 to 03 86135
20.0%
10 to 12 81498
18.9%
13 to 15 30810
 
7.1%
16 to 18 12122
 
2.8%
19 to 21 5534
 
1.3%
22 to 24 3551
 
0.8%
01 to 05 2176
 
0.5%
06 to 10 1984
 
0.5%
Other values (15) 4615
 
1.1%

Length

2023-03-19T09:01:20.266432image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
to 431101
33.3%
06 109059
 
8.4%
04 107075
 
8.3%
07 95601
 
7.4%
09 95601
 
7.4%
01 88311
 
6.8%
03 86135
 
6.7%
10 83482
 
6.5%
12 81498
 
6.3%
15 31803
 
2.5%
Other values (30) 83637
 
6.5%

Most occurring characters

ValueCountFrequency (%)
862202
25.0%
0 668658
19.4%
t 431101
12.5%
o 431101
12.5%
1 353709
10.3%
6 121665
 
3.5%
3 119393
 
3.5%
4 111144
 
3.2%
2 102311
 
3.0%
9 101356
 
2.9%
Other values (3) 146168
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1724404
50.0%
Space Separator 862202
25.0%
Lowercase Letter 862202
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 668658
38.8%
1 353709
20.5%
6 121665
 
7.1%
3 119393
 
6.9%
4 111144
 
6.4%
2 102311
 
5.9%
9 101356
 
5.9%
7 97453
 
5.7%
5 35709
 
2.1%
8 13006
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
t 431101
50.0%
o 431101
50.0%
Space Separator
ValueCountFrequency (%)
862202
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2586606
75.0%
Latin 862202
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
862202
33.3%
0 668658
25.9%
1 353709
13.7%
6 121665
 
4.7%
3 119393
 
4.6%
4 111144
 
4.3%
2 102311
 
4.0%
9 101356
 
3.9%
7 97453
 
3.8%
5 35709
 
1.4%
Latin
ValueCountFrequency (%)
t 431101
50.0%
o 431101
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3448808
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
862202
25.0%
0 668658
19.4%
t 431101
12.5%
o 431101
12.5%
1 353709
10.3%
6 121665
 
3.5%
3 119393
 
3.5%
4 111144
 
3.2%
2 102311
 
3.0%
9 101356
 
2.9%
Other values (3) 146168
 
4.2%

floor_area_sqm
Real number (ℝ)

Distinct187
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.983167
Minimum31
Maximum280
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2023-03-19T09:01:20.340050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum31
5-th percentile62
Q174
median99
Q3114
95-th percentile144
Maximum280
Range249
Interquartile range (IQR)40

Descriptive statistics

Standard deviation25.191689
Coefficient of variation (CV)0.25975321
Kurtosis-0.31839554
Mean96.983167
Median Absolute Deviation (MAD)21
Skewness0.29674959
Sum41809540
Variance634.6212
MonotonicityNot monotonic
2023-03-19T09:01:20.422898image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
67 29002
 
6.7%
104 20807
 
4.8%
110 16786
 
3.9%
68 16639
 
3.9%
84 14904
 
3.5%
121 13742
 
3.2%
91 13148
 
3.0%
92 12748
 
3.0%
73 12330
 
2.9%
103 12286
 
2.8%
Other values (177) 268709
62.3%
ValueCountFrequency (%)
31 281
0.1%
34 52
 
< 0.1%
35 19
 
< 0.1%
37 2
 
< 0.1%
38 16
 
< 0.1%
39 21
 
< 0.1%
40 200
< 0.1%
41 55
 
< 0.1%
42 344
0.1%
43 205
< 0.1%
ValueCountFrequency (%)
280 3
< 0.1%
266 3
< 0.1%
261 1
 
< 0.1%
259 2
 
< 0.1%
250 1
 
< 0.1%
249 3
< 0.1%
243 5
< 0.1%
241 4
< 0.1%
239 1
 
< 0.1%
237 2
 
< 0.1%

flat_model
Categorical

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.6 MiB
model a
127361 
improved
113721 
new generation
78234 
premium apartment
28335 
simplified
24717 
Other values (15)
58733 

Length

Max length22
Median length19
Mean length9.6152247
Min length4

Characters and Unicode

Total characters4145133
Distinct characters24
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmodel a
2nd rowmodel a
3rd rowapartment
4th rowmodel a
5th rowmodel a

Common Values

ValueCountFrequency (%)
model a 127361
29.5%
improved 113721
26.4%
new generation 78234
18.1%
premium apartment 28335
 
6.6%
simplified 24717
 
5.7%
apartment 17789
 
4.1%
standard 17761
 
4.1%
maisonette 11905
 
2.8%
model a2 7366
 
1.7%
dbss 1380
 
0.3%
Other values (10) 2532
 
0.6%

Length

2023-03-19T09:01:20.504601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
model 135452
20.1%
a 128086
19.0%
improved 113772
16.8%
generation 78419
11.6%
new 78234
11.6%
apartment 46150
 
6.8%
premium 28428
 
4.2%
simplified 24717
 
3.7%
standard 17761
 
2.6%
maisonette 12748
 
1.9%
Other values (12) 11601
 
1.7%

Most occurring characters

ValueCountFrequency (%)
e 610858
14.7%
m 389899
9.4%
a 356395
8.6%
o 341276
8.2%
n 312552
7.5%
d 312485
7.5%
i 308524
7.4%
r 285173
 
6.9%
244267
 
5.9%
t 215646
 
5.2%
Other values (14) 768058
18.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3893155
93.9%
Space Separator 244267
 
5.9%
Decimal Number 7711
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 610858
15.7%
m 389899
10.0%
a 356395
9.2%
o 341276
8.8%
n 312552
8.0%
d 312485
8.0%
i 308524
7.9%
r 285173
7.3%
t 215646
 
5.5%
p 213393
 
5.5%
Other values (11) 546954
14.0%
Decimal Number
ValueCountFrequency (%)
2 7491
97.1%
1 220
 
2.9%
Space Separator
ValueCountFrequency (%)
244267
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3893155
93.9%
Common 251978
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 610858
15.7%
m 389899
10.0%
a 356395
9.2%
o 341276
8.8%
n 312552
8.0%
d 312485
8.0%
i 308524
7.9%
r 285173
7.3%
t 215646
 
5.5%
p 213393
 
5.5%
Other values (11) 546954
14.0%
Common
ValueCountFrequency (%)
244267
96.9%
2 7491
 
3.0%
1 220
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4145133
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 610858
14.7%
m 389899
9.4%
a 356395
8.6%
o 341276
8.2%
n 312552
7.5%
d 312485
7.5%
i 308524
7.4%
r 285173
 
6.9%
244267
 
5.9%
t 215646
 
5.2%
Other values (14) 768058
18.5%

lease_commence_date
Real number (ℝ)

Distinct54
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1989.4208
Minimum1966
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.6 MiB
2023-03-19T09:01:20.592010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1966
5-th percentile1974
Q11983
median1988
Q31997
95-th percentile2005
Maximum2019
Range53
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.25101
Coefficient of variation (CV)0.005152761
Kurtosis-0.45105595
Mean1989.4208
Median Absolute Deviation (MAD)8
Skewness0.18675609
Sum8.5764128 × 108
Variance105.0832
MonotonicityNot monotonic
2023-03-19T09:01:20.798697image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1985 34660
 
8.0%
1984 25031
 
5.8%
1997 22678
 
5.3%
1988 20915
 
4.9%
1996 19061
 
4.4%
1998 18662
 
4.3%
1987 17401
 
4.0%
1978 16090
 
3.7%
1986 15369
 
3.6%
1999 14692
 
3.4%
Other values (44) 226542
52.5%
ValueCountFrequency (%)
1966 21
 
< 0.1%
1967 3018
0.7%
1968 828
 
0.2%
1969 3128
0.7%
1970 5001
1.2%
1971 3265
0.8%
1972 2464
 
0.6%
1973 3551
0.8%
1974 5525
1.3%
1975 7184
1.7%
ValueCountFrequency (%)
2019 2
 
< 0.1%
2018 2
 
< 0.1%
2017 40
 
< 0.1%
2016 1359
 
0.3%
2015 4142
1.0%
2014 1568
 
0.4%
2013 2812
0.7%
2012 2664
0.6%
2011 1434
 
0.3%
2010 823
 
0.2%

Interactions

2023-03-19T09:01:11.864003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-03-19T09:00:59.460528image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-19T09:01:01.063103image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-19T09:01:02.736891image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2023-03-19T09:00:57.674002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-19T09:00:59.343177image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-19T09:01:00.942753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-19T09:01:02.619131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-19T09:01:04.162099image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-19T09:01:05.736907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-19T09:01:08.158002image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-19T09:01:09.764425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-03-19T09:01:11.703644image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2023-03-19T09:01:20.895807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
resale_pricedistance_to_mrt_kmpopulation_countadult_countchildren_countsenior_citizen_countteenager_countyoung_adult_countfemale_countmale_countmale_female_ratiofloor_area_sqmlease_commence_datemrt_lrt_linksmrt_interchange_flagmrt_interchange_countdistance_to_mrt_binscodes_namemale_female_ratio_binspopulation_binstownflat_typestorey_rangeflat_model
resale_price1.000-0.1070.0190.0180.094-0.1020.022-0.0080.0230.014-0.0610.5960.4380.0650.0660.0660.0250.0930.0110.0920.1470.2780.1780.254
distance_to_mrt_km-0.1071.0000.0550.044-0.0290.1340.0570.0700.0540.0520.0500.003-0.1760.0880.0960.0960.9310.4230.0310.1540.3660.0640.0380.105
population_count0.0190.0551.0000.9980.9440.7220.9670.9700.9990.9990.2550.2160.2410.1110.2360.2360.1020.3060.0420.8860.6010.1010.0660.171
adult_count0.0180.0440.9981.0000.9450.7170.9620.9660.9980.9980.2540.2100.2460.1150.2240.2240.1080.3350.0430.8920.6180.1020.0660.168
children_count0.094-0.0290.9440.9451.0000.5570.9210.8930.9400.9430.2850.2530.3930.1460.2080.2080.1260.3350.0420.8890.6040.1280.0670.192
senior_citizen_count-0.1020.1340.7220.7170.5571.0000.6080.6660.7370.713-0.181-0.093-0.2610.1090.2000.2000.1060.2890.0620.5850.5130.0870.0500.143
teenager_count0.0220.0570.9670.9620.9210.6081.0000.9730.9620.9700.3530.2870.2990.1070.1800.1800.1300.3590.0350.9390.6380.1380.0730.190
young_adult_count-0.0080.0700.9700.9660.8930.6660.9731.0000.9650.9730.3310.2480.2360.1010.2200.2200.0830.3230.0331.0000.6570.1260.0740.189
female_count0.0230.0540.9990.9980.9400.7370.9620.9651.0000.9970.2290.2110.2330.1140.2740.2740.1010.3140.0440.8890.6250.1010.0670.168
male_count0.0140.0520.9990.9980.9430.7130.9700.9730.9971.0000.2790.2170.2440.1190.2240.2240.1170.3140.0410.9650.6340.1300.0670.195
male_female_ratio-0.0610.0500.2550.2540.285-0.1810.3530.3310.2290.2791.0000.2200.3540.0520.0390.0390.0450.1971.0000.0660.4310.0750.0400.109
floor_area_sqm0.5960.0030.2160.2100.253-0.0930.2870.2480.2110.2170.2201.0000.5000.0790.0530.0530.0530.0890.0230.0900.1790.7220.0510.409
lease_commence_date0.438-0.1760.2410.2460.393-0.2610.2990.2360.2330.2440.3540.5001.0000.1490.1350.1350.1210.2450.0440.2340.4530.2890.1350.408
mrt_lrt_links0.0650.0880.1110.1150.1460.1090.1070.1010.1140.1190.0520.0790.1491.0000.0390.0390.0360.4330.0010.0350.2980.0760.0350.090
mrt_interchange_flag0.0660.0960.2360.2240.2080.2000.1800.2200.2740.2240.0390.0530.1350.0391.0001.0000.0260.3960.0060.1000.5330.0290.0350.118
mrt_interchange_count0.0660.0960.2360.2240.2080.2000.1800.2200.2740.2240.0390.0530.1350.0391.0001.0000.0260.3960.0060.1000.5330.0290.0350.118
distance_to_mrt_bins0.0250.9310.1020.1080.1260.1060.1300.0830.1010.1170.0450.0530.1210.0360.0260.0261.0000.7220.0040.0680.3360.0800.0240.090
codes_name0.0930.4230.3060.3350.3350.2890.3590.3230.3140.3140.1970.0890.2450.4330.3960.3960.7221.0000.0510.3630.6190.1000.0580.139
male_female_ratio_bins0.0110.0310.0420.0430.0420.0620.0350.0330.0440.0411.0000.0230.0440.0010.0060.0060.0040.0511.0000.0100.0910.0180.0040.038
population_bins0.0920.1540.8860.8920.8890.5850.9391.0000.8890.9650.0660.0900.2340.0350.1000.1000.0680.3630.0101.0000.6740.0860.0900.172
town0.1470.3660.6010.6180.6040.5130.6380.6570.6250.6340.4310.1790.4530.2980.5330.5330.3360.6190.0910.6741.0000.2110.0720.235
flat_type0.2780.0640.1010.1020.1280.0870.1380.1260.1010.1300.0750.7220.2890.0760.0290.0290.0800.1000.0180.0860.2111.0000.0720.665
storey_range0.1780.0380.0660.0660.0670.0500.0730.0740.0670.0670.0400.0510.1350.0350.0350.0350.0240.0580.0040.0900.0720.0721.0000.112
flat_model0.2540.1050.1710.1680.1920.1430.1900.1890.1680.1950.1090.4090.4080.0900.1180.1180.0900.1390.0380.1720.2350.6650.1121.000

Missing values

2023-03-19T09:01:13.810307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-19T09:01:14.845890image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

resale_pricedistance_to_mrt_kmmrt_lrt_linksmrt_interchange_flagmrt_interchange_countdistance_to_mrt_binscodes_namepopulation_countadult_countchildren_countsenior_citizen_countteenager_countyoung_adult_countfemale_countmale_countmale_female_ratiomale_female_ratio_binspopulation_binsmonthtownflat_typeblockstreet_namestorey_rangefloor_area_sqmflat_modellease_commence_date
0209700.01.1376510000EW59510.030090.05050.02840.08970.010450.029980.029530.00.984990212001-08pasir ris4-room440pasir ris drive 401 to 03118.0model a1989
1204300.00.9162270000EW59510.030090.05050.02840.08970.010450.029980.029530.00.984990212002-09pasir ris4-room473pasir ris drive 604 to 06103.0model a1989
2553500.01.3448880001EW59510.030090.05050.02840.08970.010450.029980.029530.00.984990212020-10pasir risexecutive220pasir ris street 2110 to 12152.0apartment1993
3315000.00.8411720000EW59510.030090.05050.02840.08970.010450.029980.029530.00.984990212010-10pasir ris4-room474pasir ris drive 604 to 06105.0model a1989
4219600.01.7748090001EW36350.018880.03450.01550.06190.05320.018380.017970.00.977693202002-08pasir ris4-room751pasir ris street 7104 to 06104.0model a1996
5310500.01.1502090000EW59510.030090.05050.02840.08970.010450.029980.029530.00.984990212009-04pasir ris5-room102pasir ris street 1204 to 06122.0improved1988
6333000.00.8078830000EW59510.030090.05050.02840.08970.010450.029980.029530.00.984990212019-03pasir ris4-room114pasir ris street 1101 to 03106.0model a1989
7391500.00.7225140000EW59510.030090.05050.02840.08970.010450.029980.029530.00.984990212017-02pasir ris4-room416pasir ris drive 607 to 09105.0model a1989
8264600.01.6890870001EW36350.018880.03450.01550.06190.05320.018380.017970.00.977693202000-05pasir ris4-room746pasir ris street 7104 to 06108.0model a1996
9346500.01.1502090000EW59510.030090.05050.02840.08970.010450.029980.029530.00.984990212015-05pasir ris4-room102pasir ris street 1201 to 03104.0model a1988
resale_pricedistance_to_mrt_kmmrt_lrt_linksmrt_interchange_flagmrt_interchange_countdistance_to_mrt_binscodes_namepopulation_countadult_countchildren_countsenior_citizen_countteenager_countyoung_adult_countfemale_countmale_countmale_female_ratiomale_female_ratio_binspopulation_binsmonthtownflat_typeblockstreet_namestorey_rangefloor_area_sqmflat_modellease_commence_date
431091208800.00.1984860000BP20240.010620.02980.0860.02580.02530.010150.010090.00.994089202004-06bukit panjang4-room605senja road01 to 0399.0model a1999
431092562500.00.1984860000BP20240.010620.02980.0860.02580.02530.010150.010090.00.994089202017-02bukit panjang5-room605senja road28 to 30121.0improved1999
431093514800.00.1984860000BP20240.010620.02980.0860.02580.02530.010150.010090.00.994089202012-09bukit panjang5-room605senja road10 to 12120.0improved1999
431094243900.00.1984860000BP20240.010620.02980.0860.02580.02530.010150.010090.00.994089202006-06bukit panjang4-room605senja road25 to 2799.0model a1999
431095208800.00.1984860000BP20240.010620.02980.0860.02580.02530.010150.010090.00.994089202003-10bukit panjang4-room605senja road07 to 0999.0model a1999
431096477000.00.1984860000BP20240.010620.02980.0860.02580.02530.010150.010090.00.994089202017-03bukit panjang4-room605senja road13 to 1599.0model a1999
431097546030.00.1984860000BP20240.010620.02980.0860.02580.02530.010150.010090.00.994089202013-01bukit panjang5-room605senja road07 to 09121.0improved1999
431098218700.00.1984860000BP20240.010620.02980.0860.02580.02530.010150.010090.00.994089202003-11bukit panjang4-room605senja road07 to 0999.0model a1999
431099567000.00.1984860000BP20240.010620.02980.0860.02580.02530.010150.010090.00.994089202014-07bukit panjang5-room605senja road28 to 30121.0improved1999
431100257400.00.1984860000BP20240.010620.02980.0860.02580.02530.010150.010090.00.994089202006-02bukit panjang5-room605senja road07 to 09120.0improved1999